Search Results for author: Salam Khalifa

Found 18 papers, 2 papers with code

UniMorph 4.0: Universal Morphology

no code implementations7 May 2022 Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova

The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.

Morphological Inflection

Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects

1 code implementation Findings (ACL) 2022 Go Inoue, Salam Khalifa, Nizar Habash

We present state-of-the-art results on morphosyntactic tagging across different varieties of Arabic using fine-tuned pre-trained transformer language models.

A Spelling Correction Corpus for Multiple Arabic Dialects

no code implementations LREC 2020 Fadhl Eryani, Nizar Habash, Houda Bouamor, Salam Khalifa

In this paper, we present the MADAR CODA Corpus, a collection of 10, 000 sentences from five Arabic city dialects (Beirut, Cairo, Doha, Rabat, and Tunis) represented in the Conventional Orthography for Dialectal Arabic (CODA) in parallel with their raw original form.

Spelling Correction

A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance

no code implementations WS 2019 Alex Erdmann, er, Salam Khalifa, Mai Oudah, Nizar Habash, Houda Bouamor

We present de-lexical segmentation, a linguistically motivated alternative to greedy or other unsupervised methods, requiring only minimal language specific input.

An Arabic Morphological Analyzer and Generator with Copious Features

no code implementations WS 2018 Dima Taji, Salam Khalifa, Ossama Obeid, Fadhl Eryani, Nizar Habash

We introduce CALIMA-Star, a very rich Arabic morphological analyzer and generator that provides functional and form-based morphological features as well as built-in tokenization, phonological representation, lexical rationality and much more.

CamelParser: A system for Arabic Syntactic Analysis and Morphological Disambiguation

no code implementations COLING 2016 Anas Shahrour, Salam Khalifa, Dima Taji, Nizar Habash

In this paper, we present CamelParser, a state-of-the-art system for Arabic syntactic dependency analysis aligned with contextually disambiguated morphological features.

Dependency Parsing Morphological Analysis +2

A Large Scale Corpus of Gulf Arabic

no code implementations LREC 2016 Salam Khalifa, Nizar Habash, Dana Abdulrahim, Sara Hassan

Most Arabic natural language processing tools and resources are developed to serve Modern Standard Arabic (MSA), which is the official written language in the Arab World.

DALILA: The Dialectal Arabic Linguistic Learning Assistant

no code implementations LREC 2016 Salam Khalifa, Houda Bouamor, Nizar Habash

Dialectal Arabic (DA) poses serious challenges for Natural Language Processing (NLP).

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